Summarization
Transformers
PyTorch
Safetensors
Italian
t5
text2text-generation
text-generation-inference
Instructions to use ARTeLab/it5-summarization-fanpage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ARTeLab/it5-summarization-fanpage with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="ARTeLab/it5-summarization-fanpage")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ARTeLab/it5-summarization-fanpage") model = AutoModelForSeq2SeqLM.from_pretrained("ARTeLab/it5-summarization-fanpage") - Notebooks
- Google Colab
- Kaggle
| { | |
| "epoch": 4.0, | |
| "eval_gen_len": 69.7954, | |
| "eval_loss": 1.549155831336975, | |
| "eval_rouge1": 33.8286, | |
| "eval_rouge2": 15.4551, | |
| "eval_rougeL": 24.9001, | |
| "eval_rougeLsum": 28.3133, | |
| "eval_runtime": 3492.8584, | |
| "eval_samples": 8436, | |
| "eval_samples_per_second": 2.415, | |
| "eval_steps_per_second": 0.805, | |
| "predict_gen_len": 69.3041, | |
| "predict_loss": 1.5348224639892578, | |
| "predict_rouge1": 34.1882, | |
| "predict_rouge2": 15.7866, | |
| "predict_rougeL": 25.141, | |
| "predict_rougeLsum": 28.4882, | |
| "predict_runtime": 3479.9848, | |
| "predict_samples": 8437, | |
| "predict_samples_per_second": 2.424, | |
| "predict_steps_per_second": 0.808, | |
| "train_loss": 1.6458884161285257, | |
| "train_runtime": 76560.3935, | |
| "train_samples": 67492, | |
| "train_samples_per_second": 3.526, | |
| "train_steps_per_second": 1.175 | |
| } |